What is gipc?

Naive usage of Python's multiprocessing package in the context of a
gevent-powered application may raise various problems and most likely breaks the
application in many ways.

gipc (pronunciation "gipsy") is developed with the motivation to solve these
issues transparently. With gipc, multiprocessing.Process-based child processes
can safely be created anywhere within your gevent-powered application. The API
of multiprocessing.Process objects is provided in a gevent-cooperative fashion.
Furthermore, gipc comes up with a pipe-based transport layer for
gevent-cooperative inter-process communication and useful helper constructs.
gipc is lightweight and simple to integrate.

What are the boundary conditions?

Currently, gipc is developed against gevent 1.0rc2. It is tested on CPython 2.6
& 2.7 on Linux as well as on Windows.

Where are documentation and changelog?

The API documentation and further details can be found at
http://gehrcke.de/gipc.
Here, the
changelog can be retrieved from Bitbucket.

Is gipc stable?

Development began in late 2012, so it is far from being mature. However, as of
version 0.3.0, I am not aware of any fundamental issue. gipc's basic approach
has proven to be reasonable. gipc is developed with a strong focus on
reliability and with best intentions in mind. Via extensive unit testing, it has
been validated to work reliably in scenarios of low and medium complexity. It is
ready to be evaluated in the context of serious projects. Please let me know how
gipc performs for you.

Where should I download gipc?

Releases are available at PyPI.
The development version can be retrieved from the Mercurial repository at
Bitbucket.

How can the unit tests be run?

If you run into troubles with gipc, it is a good idea to run the unit test suite
under your conditions. gipc's unit tests are written for
pytest. With gipc/test (included in the release)
being the current working directory, I usually run tests like this:

$ py.test -v

How is code audit perfomed?

I use pep8 and
pylint. Have a look at audit.sh in
the code repository. Unit test code coverage analysis requires
coverage and
pytest-cov. audit.sh leaves
behind a coverage HTML report in the coverage_html directory.